On the other hand, multi-task learning splits the training process into a set of dependent tasks and optimizes all tasks jointly. For our continual neural mapping setting, the task boundary is unknown, thus requiring a continual task identifier that assigns training data to specific tasks consistently over time. Meanwhile, backward transfer addresses the problem of constant network adaptation for all tasks, which is opposed to conventional multi-task learning that fixes the network once the model is deployed [1]}. The fine-tuning strategy maintains a single network consecutively, where network parameters of a new task are initialized with that of the last task. However, as neural networks tend to be overly plastic [2]} from the "plasticity-stability dilemma" perspective [3]}, the performance of early tasks will degrade on current network parameters (Fig. REF ), namely catastrophic forgetting [4]}. Finally, batch re-training preserves all previously observed data \(\mathbf {x}^{1:t}\)  to satisfy the iid-sampled assumption. However, batch re-training learns a new model at each time from scratch without exploiting past experience. The linearly-growing number of training data results in expensive memory consumption and computational cost.
